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In the Multiple Regression Model with Two Explanatory Variables Yi=β0+β1X1i+β2X2i+uiY _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 i } + \beta _ { 2 } X _ { 2 i } + u _ { i }

question 25

Essay

In the multiple regression model with two explanatory variables Yi=β0+β1X1i+β2X2i+uiY _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 i } + \beta _ { 2 } X _ { 2 i } + u _ { i } the OLS estimators for the three parameters are as follows (small letters refer to deviations from means as in zi=ZiZˉz _ { i } = Z _ { i } - \bar { Z } ):
β^0=Yˉβ^1Xˉ1β^2Xˉ2β^1=i=1nyix1ii=1nx2i2i=1nyix2ii=1nx1ix2ii=1nx1i2i=1nx2i2(i=1nx1ix2i)2β^2=i=1nyix2ii=1nx1i2i=1nyix1ii=1nx1ix2ii=1nx1i2i=1nx2i2(i=1nx1ix2i)2\begin{array} { c } \hat { \beta } _ { 0 } = \bar { Y } - \hat { \beta } _ { 1 } \bar { X } _ { 1 } - \hat { \beta } _ { 2 } \bar { X } _ { 2 } \\\hat { \beta } _ { 1 } = \frac { \sum _ { i = 1 } ^ { n } y _ { i } x _ { 1 i } \sum _ { i = 1 } ^ { n } x _ { 2 i } ^ { 2 } - \sum _ { i = 1 } ^ { n } y _ { i } x _ { 2 i } \sum _ { i = 1 } ^ { n } x _ { 1 i } x _ { 2 i } } { \sum _ { i = 1 } ^ { n } x _ { 1 i } ^ { 2 } \sum _ { i = 1 } ^ { n } x _ { 2 i } ^ { 2 } - \left( \sum _ { i = 1 } ^ { n } x _ { 1 i } x _ { 2 i } \right) ^ { 2 } } \\\\\hat { \beta } _ { 2 } = \frac { \sum _ { i = 1 } ^ { n } y _ { i } x _ { 2 i } \sum _ { i = 1 } ^ { n } x _ { 1 i } ^ { 2 } - \sum _ { i = 1 } ^ { n } y _ { i } x _ { 1 i } \sum _ { i = 1 } ^ { n } x _ { 1 i } x _ { 2 i } } { \sum _ { i = 1 } ^ { n } x _ { 1 i } ^ { 2 } \sum _ { i = 1 } ^ { n } x _ { 2 i } ^ { 2 } - \left( \sum _ { i = 1 } ^ { n } x _ { 1 i } x _ { 2 i } \right) ^ { 2 } }\end{array} You have collected data for 104 countries of the world from the Penn World Tables and want to estimate the effect of the population growth rate (X1i)\left( X _ { 1 i } \right) and the saving rate (X2i)\left( X _ { 2 i } \right) (average investment share of GDP from 1980 to 1990) on GDP per worker (relative to the U.S.) in 1990. The various sums needed to calculate the OLS estimates are given below: i=1nYi=33.33;i=1nX1i=2.025;i=1nX2i=17.313i=1nyi2=8.3103;i=1nx1i2=.0122;i=1nx2i2=0.6422\begin{array} { l } \sum _ { i = 1 } ^ { n } Y _ { i } = 33.33 ; \sum _ { i = 1 } ^ { n } X _ { 1 i } = 2.025 ; \sum _ { i = 1 } ^ { n } X _ { 2 i } = 17.313 \\\\\sum _ { i = 1 } ^ { n } y _ { i } ^ { 2 } = 8.3103 ; \sum _ { i = 1 } ^ { n } x _ { 1 i } ^ { 2 } = .0122 ; \sum _ { i = 1 } ^ { n } x _ { 2 i } ^ { 2 } = 0.6422\end{array} i=1nyix1i=0.2304;i=1nyix2i=1.5676;i=1nx1ix2i=0.0520\sum _ { i = 1 } ^ { n } y _ { i } x _ { 1 i } = - 0.2304 ; \sum _ { i = 1 } ^ { n } y _ { i } x _ { 2 i } = 1.5676 ; \sum _ { i = 1 } ^ { n } x _ { 1 i } x _ { 2 i } = - 0.0520 (a)What are your expected signs for the regression coefficient? Calculate the coefficients
and see if their signs correspond to your intuition.

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Comprehend the role and impact of significant figures in post-WWII geopolitical changes and policies.
Evaluate the impact of WWII and the Cold War on civilian populations and societal structures.
Grasp the technological advancements and their societal impacts in the post-WWII era.

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